OpenRaiser/Intern-Atlas huggingface dataset:
Analysis of Feedback Error in Automatic Repeat reQuest
Venue: arXiv cs.IT 2017
Year: 2017

The future wireless networks envision ultra-reliable communication with efficient use of the limited wireless channel resources. Closed-loop repetition protocols where retransmission of a packet is enabled using a feedback channel has been adopted since early days of wireless telecommunication. Protocols such as automatic repeat request (ARQ) are used in today's wireless technologies as a mean to provide the link with reduced rate of packet outage and increased average throughput. The performance of such protocols is strongly dependent to the feedback channel reliability. This paper studies the problem of feedback error and proposes a new method of acknowledging packet delivery for retransmission protocols in unreliable feedback channel conditions. The proposed method is based on backwards composite acknowledgment from multiple packets in a retransmission protocol and provides the scheduler of the wireless channel with additional parameters to configure ultra-reliable communication for a user depending on channel quality. Numerical analysis are presented which show orders of magnitude increase in reliability of the proposed method as compared to ARQ at the cost of a small increase in average experienced delay.
Sensor Synthesis for POMDPs with Reachability Objectives
Venue: arXiv cs.AI 2017
Year: 2017

Partially observable Markov decision processes (POMDPs) are widely used in probabilistic planning problems in which an agent interacts with an environment using noisy and imprecise sensors. We study a setting in which the sensors are only partially defined and the goal is to synthesize "weakest" additional sensors, such that in the resulting POMDP, there is a small-memory policy for the agent that almost-surely (with probability~1) satisfies a reachability objective. We show that the problem is NP-complete, and present a symbolic algorithm by encoding the problem into SAT instances. We illustrate trade-offs between the amount of memory of the policy and the number of additional sensors on a simple example. We have implemented our approach and consider three classical POMDP examples from the literature, and show that in all the examples the number of sensors can be significantly decreased (as compared to the existing solutions in the literature) without increasing the complexity of the policies.
Synthesising Evolutionarily Stable Normative Systems
Venue: arXiv cs.MA 2017
Year: 2017

Within the area of multi-agent systems, normative systems are a widely used framework for the coordination of interdependent activities. A crucial problem associated with normative systems is that of synthesising norms that effectively accomplish a coordination task and whose compliance forms a rational choice for the agents within the system. In this work, we introduce a framework for the synthesis of normative systems that effectively coordinate a multi-agent system and whose norms are likely to be adopted by rational agents. Our approach roots in evolutionary game theory. Our framework considers multi-agent systems in which evolutionary forces lead successful norms to prosper and spread within the agent population, while unsuccessful norms are discarded. The outputs of this evolutionary norm synthesis process are normative systems whose compliance forms a rational choice for the agents. We empirically show the effectiveness of our approach through empirical evaluation in a simulated traffic domain.
DFT-Spread OFDM with Frequency Domain Reference Symbols
Venue: arXiv cs.IT 2017
Year: 2017

The fifth generation (5G) wireless standard will support several new use cases and 10 to 100 times the performance of fourth generation (4G) systems. Because of the diverse applications for 5G, flexible solutions which can address conflicting requirements will be needed. In this paper, we propose a solution which enables the use of discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) and OFDM, which address different requirements, using a common reference symbol (RS) design. In this solution, the DFT-s-OFDM symbol contains RSs in the frequency domain that may be shared by a subsequent OFDM symbol. The proposed scheme is generated by puncturing the output of a DFT-spread block and replacing the punctured samples with RSs in frequency. We prove that puncturing the interleaved samples at the output of the DFT-spread operation equivalently introduces a periodic interference to the data symbols at the input of the DFT-spread operation. We show that the interference due to the puncturing can be removed with a low-complexity receiver by exploiting the zeros inserted to certain locations before the DFT-spread block at the transmitter. Simulation results support that the proposed scheme removes the error floor caused by the puncturing and achieves lower peak-to-average-power ratio than OFDM.
Elastic-Net: Boosting Energy Efficiency and Resource Utilization in 5G C-RANs
Venue: arXiv cs.NI 2017
Year: 2017

Current Distributed Radio Access Networks~(D-RANs), which are characterized by a static configuration and deployment of Base Stations~(BSs), have exposed their limitations in handling the temporal and geographical fluctuations of capacity demands. At the same time, each BS's spectrum and computing resources are only used by the active users in the cell range, causing idle BSs in some areas/times and overloaded BSs in other areas/times. Recently, Cloud Radio Access Network~(C-RAN) has been introduced as a new centralized paradigm for wireless cellular networks in which---through virtualization---the BSs are physically decoupled into Virtual Base Stations~(VBSs) and Remote Radio Heads~(RRHs). In this paper, a novel elastic framework aimed at fully exploiting the potential of C-RAN is proposed, which is able to adapt to the fluctuation in capacity demand while at the same time maximizing the energy efficiency and resource utilization. Simulation and testbed experiment results are presented to illustrate the performance gains of the proposed elastic solution against the current static deployment.
PIRVS: An Advanced Visual-Inertial SLAM System with Flexible Sensor Fusion and Hardware Co-Design
Venue: arXiv cs.RO 2017
Year: 2017

In this paper, we present the PerceptIn Robotics Vision System (PIRVS) system, a visual-inertial computing hardware with embedded simultaneous localization and mapping (SLAM) algorithm. The PIRVS hardware is equipped with a multi-core processor, a global-shutter stereo camera, and an IMU with precise hardware synchronization. The PIRVS software features a novel and flexible sensor fusion approach to not only tightly integrate visual measurements with inertial measurements and also to loosely couple with additional sensor modalities. It runs in real-time on both PC and the PIRVS hardware. We perform a thorough evaluation of the proposed system using multiple public visual-inertial datasets. Experimental results demonstrate that our system reaches comparable accuracy of state-of-the-art visual-inertial algorithms on PC, while being more efficient on the PIRVS hardware.
The SysML/KAOS Domain Modeling Approach
Venue: arXiv cs.SE 2017
Year: 2017

A means of building safe critical systems consists of formally modeling the requirements formulated by stakeholders and ensuring their consistency with respect to application domain properties. This paper proposes a metamodel for an ontology modeling formalism based on OWL and PLIB. This modeling formalism is part of a method for modeling the domain of systems whose requirements are captured through SysML/KAOS. The formal semantics of SysML/KAOS goals are represented using Event-B specifications. Goals provide the set of events, while domain models will provide the structure of the system state of the Event-B specification. Our proposal is illustrated through a case study dealing with a Cycab localization component specification. The case study deals with the specification of a localization software component that uses GPS,Wi-Fi and sensor technologies for the realtime localization of the Cycab vehicle, an autonomous ground transportation system designed to be robust and completely independent.
End-to-end Learning for 3D Facial Animation from Raw Waveforms of Speech
Venue: arXiv cs.CV 2017
Year: 2017

We present a deep learning framework for real-time speech-driven 3D facial animation from just raw waveforms. Our deep neural network directly maps an input sequence of speech audio to a series of micro facial action unit activations and head rotations to drive a 3D blendshape face model. In particular, our deep model is able to learn the latent representations of time-varying contextual information and affective states within the speech. Hence, our model not only activates appropriate facial action units at inference to depict different utterance generating actions, in the form of lip movements, but also, without any assumption, automatically estimates emotional intensity of the speaker and reproduces her ever-changing affective states by adjusting strength of facial unit activations. For example, in a happy speech, the mouth opens wider than normal, while other facial units are relaxed; or in a surprised state, both eyebrows raise higher. Experiments on a diverse audiovisual corpus of different actors across a wide range of emotional states show interesting and promising results of our approach. Being speaker-independent, our generalized model is readily applicable to various tasks in human-machine interaction and animation.
A concatenating framework of shortcut convolutional neural networks
Venue: arXiv cs.CV 2017
Year: 2017

It is well accepted that convolutional neural networks play an important role in learning excellent features for image classification and recognition. However, in tradition they only allow adjacent layers connected, limiting integration of multi-scale information. To further improve their performance, we present a concatenating framework of shortcut convolutional neural networks. This framework can concatenate multi-scale features by shortcut connections to the fully-connected layer that is directly fed to the output layer. We do a large number of experiments to investigate performance of the shortcut convolutional neural networks on many benchmark visual datasets for different tasks. The datasets include AR, FERET, FaceScrub, CelebA for gender classification, CUReT for texture classification, MNIST for digit recognition, and CIFAR-10 for object recognition. Experimental results show that the shortcut convolutional neural networks can achieve better results than the traditional ones on these tasks, with more stability in different settings of pooling schemes, activation functions, optimizations, initializations, kernel numbers and kernel sizes.
Towards an Inferential Lexicon of Event Selecting Predicates for French
Venue: arXiv cs.CL 2017
Year: 2017

We present a manually constructed seed lexicon encoding the inferential profiles of French event selecting predicates across different uses. The inferential profile (Karttunen, 1971a) of a verb is designed to capture the inferences triggered by the use of this verb in context. It reflects the influence of the clause-embedding verb on the factuality of the event described by the embedded clause. The resource developed provides evidence for the following three hypotheses: (i) French implicative verbs have an aspect dependent profile (their inferential profile varies with outer aspect), while factive verbs have an aspect independent profile (they keep the same inferential profile with both imperfective and perfective aspect); (ii) implicativity decreases with imperfective aspect: the inferences triggered by French implicative verbs combined with perfective aspect are often weakened when the same verbs are combined with imperfective aspect; (iii) implicativity decreases with an animate (deep) subject: the inferences triggered by a verb which is implicative with an inanimate subject are weakened when the same verb is used with an animate subject. The resource additionally shows that verbs with different inferential profiles display clearly distinct sub-categorisation patterns. In particular, verbs that have both factive and implicative readings are shown to prefer infinitival clauses in their implicative reading, and tensed clauses in their factive reading.
Finding Talk About the Past in the Discourse of Non-Historians
Venue: arXiv cs.IR 2017
Year: 2017

A heightened interest in the presence of the past has given rise to the new field of memory studies, but there is a lack of search and research tools to support studying how and why the past is evoked in diachronic discourses. Searching for temporal references is not straightforward. It entails bridging the gap between conceptually-based information needs on one side, and term-based inverted indexes on the other. Our approach enables the search for references to (intersubjective) historical periods in diachronic corpora. It consists of a semantically-enhanced search engine that is able to find references to many entities at a time, which is combined with a novel interface that invites its user to actively sculpt the search result set. Until now we have been concerned mostly with user-friendly retrieval and selection of sources, but our tool can also contribute to existing efforts to create reusable linked data from and for research in the humanities.
Finding phonemes: improving machine lip-reading
Venue: arXiv cs.CV 2017
Year: 2017

In machine lip-reading there is continued debate and research around the correct classes to be used for recognition. In this paper we use a structured approach for devising speaker-dependent viseme classes, which enables the creation of a set of phoneme-to-viseme maps where each has a different quantity of visemes ranging from two to 45. Viseme classes are based upon the mapping of articulated phonemes, which have been confused during phoneme recognition, into viseme groups. Using these maps, with the LiLIR dataset, we show the effect of changing the viseme map size in speaker-dependent machine lip-reading, measured by word recognition correctness and so demonstrate that word recognition with phoneme classifiers is not just possible, but often better than word recognition with viseme classifiers. Furthermore, there are intermediate units between visemes and phonemes which are better still.
Computing Top-k Closeness Centrality in Fully-dynamic Graphs
Venue: arXiv cs.DS 2017
Year: 2017

Closeness is a widely-studied centrality measure. Since it requires all pairwise distances, computing closeness for all nodes is infeasible for large real-world networks. However, for many applications, it is only necessary to find the k most central nodes and not all closeness values. Prior work has shown that computing the top-k nodes with highest closeness can be done much faster than computing closeness for all nodes in real-world networks. However, for networks that evolve over time, no dynamic top-k closeness algorithm exists that improves on static recomputation. In this paper, we present several techniques that allow us to efficiently compute the k nodes with highest (harmonic) closeness after an edge insertion or an edge deletion. Our algorithms use information obtained during earlier computations to omit unnecessary work. However, they do not require asymptotically more memory than the static algorithms (i. e., linear in the number of nodes). We propose separate algorithms for complex networks (which exhibit the small-world property) and networks with large diameter such as street networks, and we compare them against static recomputation on a variety of real-world networks. On many instances, our dynamic algorithms are two orders of magnitude faster than recomputation; on some large graphs, we even reach average speedups between $10^3$ and $10^4$.
Variational Grid Setting Network
Venue: arXiv cs.CV 2017
Year: 2017

We propose a new neural network architecture for automatic generation of missing characters in a Chinese font set. We call the neural network architecture the Variational Grid Setting Network which is based on the variational autoencoder (VAE) with some tweaks. The neural network model is able to generate missing characters relatively large in size ($256 \times 256$ pixels). Moreover, we show that one can use very few samples for training data set, and get a satisfied result.
Gaussian Three-Dimensional kernel SVM for Edge Detection Applications
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents a novel and uniform algorithm for edge detection based on SVM (support vector machine) with Three-dimensional Gaussian radial basis function with kernel. Because of disadvantages in traditional edge detection such as inaccurate edge location, rough edge and careless on detect soft edge. The experimental results indicate how the SVM can detect edge in efficient way. The performance of the proposed algorithm is compared with existing methods, including Sobel and canny detectors. The results show that this method is better than classical algorithm such as canny and Sobel detector.
Visual speech recognition: aligning terminologies for better understanding
Venue: arXiv cs.CV 2017
Year: 2017

We are at an exciting time for machine lipreading. Traditional research stemmed from the adaptation of audio recognition systems. But now, the computer vision community is also participating. This joining of two previously disparate areas with different perspectives on computer lipreading is creating opportunities for collaborations, but in doing so the literature is experiencing challenges in knowledge sharing due to multiple uses of terms and phrases and the range of methods for scoring results. In particular we highlight three areas with the intention to improve communication between those researching lipreading; the effects of interchanging between speech reading and lipreading; speaker dependence across train, validation, and test splits; and the use of accuracy, correctness, errors, and varying units (phonemes, visemes, words, and sentences) to measure system performance. We make recommendations as to how we can be more consistent.
Cloaker Catcher: A Client-based Cloaking Detection System
Venue: arXiv cs.CR 2017
Year: 2017

Cloaking has long been exploited by spammers for the purpose of increasing the exposure of their websites. In other words, cloaking has long served as a major malicious technique in search engine optimization (SEO). Cloaking hides the true nature of a website by delivering blatantly different content to users versus web crawlers. Recently, we have also witnessed a rising trend of employing cloaking in search engine marketing (SEM). However, detecting cloaking is challenging. Existing approaches cannot detect IP cloaking and are not suitable for detecting cloaking in SEM because their search-and-visit method leads to click fraud. In addition, they focus on detecting and measuring cloaking on the server side, but the results are not visible to users to help them avoid frauds. Our work focuses on mitigating IP cloaking and SEM cloaking, and providing client-based real-time cloaking detection services. To achieve these goals, we first propose the Simhash-based Website Model (SWM), a condensed representation of websites, which can model natural page dynamics. Based on SWM, we design and implement Cloaker Catcher, an accurate, efficient and privacy-preserving system, that consists of a server that crawls websites visited by users on demand and a client-side extension that fetches spider views of websites from the server and compares them with user views to detect cloaking. Since Cloaker Catcher checks on the client side for each real user, IP cloaking can be detected whenever it occurs and click fraud in SEM can also be prevented. Using our system, we conducted the first analysis of SEM cloaking and found that the main purpose of SEM cloakers is to provide illicit services.
Robust and Secure Resource Allocation for Full-Duplex MISO Multicarrier NOMA Systems
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we study the resource allocation algorithm design for multiple-input single-output (MISO) multicarrier non-orthogonal multiple access (MC-NOMA) systems, in which a full-duplex base station serves multiple half-duplex uplink and downlink users on the same subcarrier simultaneously. The resource allocation is optimized for maximization of the weighted system throughput while the information leakage is constrained and artificial noise is injected to guarantee secure communication in the presence of multiple potential eavesdroppers. To this end, we formulate a robust non-convex optimization problem taking into account the imperfect channel state information (CSI) of the eavesdropping channels and the quality-of-service (QoS) requirements of the legitimate users. Despite the non-convexity of the optimization problem, we solve it optimally by applying monotonic optimization which yields the optimal beamforming, artificial noise design, subcarrier allocation, and power allocation policy. The optimal resource allocation policy serves as a performance benchmark since the corresponding monotonic optimization based algorithm entails a high computational complexity. Hence, we also develop a low-complexity suboptimal resource allocation algorithm which converges to a locally optimal solution. Our simulation results reveal that the performance of the suboptimal algorithm closely approaches that of the optimal algorithm. Besides, the proposed optimal MISO NOMA system can not only ensure downlink and uplink communication security simultaneously but also provides a significant system secrecy rate improvement compared to traditional MISO orthogonal multiple access (OMA) systems and two other baseline schemes.
Smoothness-based Edge Detection using Low-SNR Camera for Robot Navigation
Venue: arXiv cs.CV 2017
Year: 2017

In the emerging advancement in the branch of autonomous robotics, the ability of a robot to efficiently localize and construct maps of its surrounding is crucial. This paper deals with utilizing thermal-infrared cameras, as opposed to conventional cameras as the primary sensor to capture images of the robot's surroundings. For localization, the images need to be further processed before feeding them to a navigational system. The main motivation of this paper was to develop an edge detection methodology capable of utilizing the low-SNR poor output from such a thermal camera and effectively detect smooth edges of the surrounding environment. The enhanced edge detector proposed in this paper takes the raw image from the thermal sensor, denoises the images, applies Canny edge detection followed by CSS method. The edges are ranked to remove any noise and only edges of the highest rank are kept. Then, the broken edges are linked by computing edge metrics and a smooth edge of the surrounding is displayed in a binary image. Several comparisons are also made in the paper between the proposed technique and the existing techniques.
Improving Scientific Workflow with Cloud Offloading
Venue: arXiv cs.DC 2017
Year: 2017

Scientific workflow is a powerful tool to streamline and organize computational steps of scientific application. This paper presents Emerald, a system that adds sophisticated cloud offloading capabilities to scientific workflows. Emerald automatically offloads computation intensive steps of scientific workflow to the cloud in order to enhance workflow performance. Emerald provides easy-to-use APIs to help developers build cloud offloading enabled scientific workflows. Evaluation showed that Emerald can effectively reduce up to 55% of execution time for scientific applications.